Papers with graph-based model
A Graph-based Model for Joint Chinese Word Segmentation and Dependency Parsing (2020.tacl-1)
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| Challenge: | Chinese word segmentation and dependency parsing suffer from error propagation . a graph-based model can integrate both tasks, but it suffers from performance limitations . |
| Approach: | They propose a graph-based model to integrate Chinese word segmentation and dependency parsing . their model achieves better performance than previous joint models . |
| Outcome: | The proposed model achieves better performance than previous joint models and state-of-the-art results in both Chinese word segmentation and dependency parsing. |
When Will the Tokens End? Graph-Based Forecasting for LLMs Output Length (2025.acl-srw)
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Grzegorz Piotrowski, Mateusz Bystroński, Mikołaj Hołysz, Jakub Binkowski, Grzegorz Chodak, Tomasz Jan Kajdanowicz
| Challenge: | Large Language Models (LLMs) are typically trained to predict the next token in a sequence. However, their internal representations encode signals that go beyond immediate next-token prediction. |
| Approach: | They propose an aggregation-based model that combines hidden states from multiple transformer layers l 8, dots, 15 using element-wise operations such as mean or sum. |
| Outcome: | The proposed model reduces NMAE by over 50% on the Alpaca dataset. |
R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning (2020.coling-main)
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| Challenge: | Concept prerequisite chain learning is an unsupervised task with no access to labeled concept pairs during training. |
| Approach: | They propose a model that uses deep learning representations to predict concept relations . they frame concept prerequisite chain learning as an unsupervised task with no labeled concept pairs . |
| Outcome: | The proposed model outperforms semi-supervised methods in terms of accuracy and F1 score. |
Document-level Relation Extraction with Dual-tier Heterogeneous Graph (2020.coling-main)
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| Challenge: | Existing methods focus on extracting relations from single sentence . document-level relation extraction requires a comprehension of the whole document . |
| Approach: | They propose a graph-based model with Dual-tier Heterogeneous Graph (DHG) for document-level relation extraction. |
| Outcome: | The proposed model achieves state-of-the-art performance on two widely used datasets. |
Neural Deepfake Detection with Factual Structure of Text (2020.emnlp-main)
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| Challenge: | Existing approaches to deepfake detection typically represent documents with coarse-grained representations, but they struggle to capture factual structures of documents. |
| Approach: | They propose a graph-based model that captures factual structures of documents for deepfake detection. |
| Outcome: | The proposed model improves strong base models built with RoBERTa on two public deepfake datasets. |
A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)
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| Challenge: | Existing methods for rumor resolution ignore intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges. |
| Approach: | They propose to identify triggering posts and exploit their characteristics to facilitate rumor verification. |
| Outcome: | The proposed model and scheme exploits rumor diffusion patterns and linguistic features to facilitate verification. |
Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering (2021.acl-long)
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| Challenge: | a new method for conversational Knowledge Base Question Answering (KBQA) uses implied entities from the conversation history to answer questions. |
| Approach: | They propose to model the implied entities of conversational KBQA by applying a graph neural network to derive a probability distribution of focal entities for each question. |
| Outcome: | The proposed model captures transitions of focal entities and performs answer ranking on two datasets. |
Graph Convolutional Networks for Event Causality Identification with Rich Document-level Structures (2021.naacl-main)
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| Challenge: | Existing models for document-level Event Causality Identification (ECI) are limited to intra-sentence contexts where event mention pairs are presented in the same sentences. |
| Approach: | They propose a deep learning model that accepts inter-sentence event mention pairs . they use interaction graphs to capture relevant connections between important objects . |
| Outcome: | The proposed model achieves state-of-the-art on two benchmark datasets. |
Keep it Surprisingly Simple: A Simple First Order Graph Based Parsing Model for Joint Morphosyntactic Parsing in Sanskrit (2020.emnlp-main)
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| Challenge: | Morphologically rich languages benefit from joint processing of morphology and syntax, as compared to pipeline architectures. |
| Approach: | They propose a graph-based model for joint morphological parsing and dependency parser in Sanskrit using the Energy based model framework. |
| Outcome: | The proposed model outperforms standalone morphological parsers in morphology and syntax parsing, and in dependency parser. |
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text (D19-1)
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| Challenge: | Existing datasets for reading comprehension tasks have been used to test the generalization of natural language understanding systems. |
| Approach: | They propose a diagnostic benchmark suite to clarify key issues related to the robustness and systematicity of NLU systems. |
| Outcome: | The proposed benchmark suite clarifies key issues related to the robustness and systematicity of NLU systems. |
AliGATr: Graph-based layout generation for form understanding (2024.findings-emnlp)
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| Challenge: | State of the art forms understanding models often rely on poorly calibrated output probabilities and low performance on relation extraction tasks. |
| Approach: | They propose a graph-based model that uses a generative objective to represent complex grid-like layouts that are often found in forms. |
| Outcome: | The proposed model performs better on the KIE and RE tasks and is more accurate than existing models. |
Leveraging Social Context for Humor Recognition and Sense of Humor Evaluation in Social Media with a New Chinese Humor Corpus - HumorWB (2024.lrec-main)
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| Challenge: | Existing humor computing research focuses on content while neglecting interaction relationships in social media. |
| Approach: | They propose a dataset which introduces social context information from social media . they propose 'humor recognition' task and 'horror evaluation task' |
| Outcome: | The proposed model incorporates social context information from social media . it shows that it is efficient and can be used to evaluate humor in real life . |